_base_ = [
    '../_base_/models/setr_mla.py',
    '../_base_/datasets/ade20k_multi_scale.py', '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_160k.py'
]
model = dict(
    backbone=dict(img_size=512,pos_embed_interp=True,drop_rate=0.,mla_channels=256,mla_index=(5,11,17,23)),
    decode_head=dict(img_size=512,mla_channels=256,mlahead_channels=128,num_classes=150),
    auxiliary_head=[
        dict(
        type='VIT_MLA_AUXIHead',
        in_channels=256,
        channels=512,
        in_index=0,
        img_size=512,
        num_classes=150,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        dict(
        type='VIT_MLA_AUXIHead',
        in_channels=256,
        channels=512,
        in_index=1,
        img_size=512,
        num_classes=150,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        dict(
        type='VIT_MLA_AUXIHead',
        in_channels=256,
        channels=512,
        in_index=2,
        img_size=512,
        num_classes=150,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        dict(
        type='VIT_MLA_AUXIHead',
        in_channels=256,
        channels=512,
        in_index=3,
        img_size=512,
        num_classes=150,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        ])

optimizer = dict(lr=0.001, weight_decay=0.0,
paramwise_cfg = dict(custom_keys={'head': dict(lr_mult=10.)})
)

crop_size = (512, 512)
test_cfg = dict(mode='slide', crop_size=crop_size, stride=(341, 341))
find_unused_parameters = True
data = dict(samples_per_gpu=1)